Top Machine Learning Engineer Jobs in San Francisco Bay Area, CA

Research, design, develop, and test machine learning and robotic applications for autonomous vehicles across the full ML lifecycle. Create novel deep learning models (CNNs, generative AI), improve perception (kinematics, intent, trajectory), integrate multi-sensor data (camera, RADAR, LiDAR) with HD maps, develop auto-labeling and large-scale data pipelines, optimize models, and debug real-time perception issues in a ride-hail fleet.

Lead the design, development, and deployment of AI software components while partnering with engineers and scientists to deliver innovative AI solutions at Capital One.

Lead design, development, and operationalization of enterprise-scale AI/ML systems across hybrid environments. Drive platform architecture, model lifecycle management, migration to cloud-native platforms, generative and agentic AI integrations, automation and governance, cross-functional collaboration, and mentorship to accelerate predictive and generative AI adoption.

Lead design and delivery of scalable, secure GCP data and ML pipelines. Build feature stores, standardized transformation libraries (Python/SQL/Beam), orchestration (Composer/Airflow), CI/CD, model training/validation/monitoring, enforce data quality/lineage/observability, optimize cost/performance, and enable self-service for data scientists with documentation and runbooks.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

As a Machine Learning Engineer, you will build and evaluate AI agents for enterprise workflows, focusing on techniques for intent detection and agent quality metrics.

As a Machine Learning Engineer, you will manage multimodal perception, work on data collection, and evaluate robotics tasks, directly impacting model capabilities and the evolution of physical labor.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Develop deep learning models for autonomous vehicle trajectory generation, collaborate with cross-functional teams, and analyze system performance metrics.

Lead the 3D Mapping team in designing ML models and algorithms for 3D perception and mapping, coordinating with cross-functional teams and driving development metrics.

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

Develop and optimize large-scale simulation and RL infrastructure for training surgical robots. Build multimodal data pipelines, generate synthetic datasets, perform sim-to-real validation, and integrate simulation outputs into production ML pipelines. Deliver production-ready code, optimize GPU/inference/rendering performance, and collaborate with clinical advisors to iterate prototypes and deploy physical AI systems.

Design, prototype, and productionize AI systems and infrastructure—recommendations, natural language interfaces, agentic workflows—partnering with product and engineering to scale ML across the full lifecycle (data curation, training, deployment, monitoring).

Build and productionize AI systems and infrastructure that power customer-facing features and internal tooling. Partner with product, design, and engineering to identify high-impact ML opportunities, prototype solutions (recommendations, NL interfaces, agents), and manage the full ML lifecycle from data curation to deployment and monitoring.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current with ML/AI advances.

Lead technical development of machine learning algorithms for the onboard Planner, advancing imitation learning, reinforcement learning, and model scaling; integrate foundation models; create metrics, data, and training frameworks; mentor ML developers; and collaborate across perception, simulation, system design, and safety teams to solve autonomous driving challenges in complex urban environments.

Develop and optimize a high-speed GPU-based simulation framework for ML training and validation. Apply reinforcement learning to solve behavior and path-planning challenges, bridge sim-to-sim and sim-to-real fidelity gaps, enable self-serve data generation for autonomy users, and write production-ready code integrating ML into core simulation architecture.

The role involves training and deploying deep learning models for semantic reasoning in autonomous vehicles, focusing on highway environments and optimizing systems for real-time performance.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.